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Issue No. 01 - February (1996 vol. 8)
ISSN: 1041-4347
pp: 120-133
<p><b>Abstract</b>—Genetic Algorithms are efficient and robust search methods that are being employed in a plethora of applications with extremely large search spaces. The directed search mechanism employed in Genetic Algorithms performs a simultaneous and balanced, exploration of new regions in the search space and exploitation of already discovered regions.</p><p>This paper introduces the notion of <it>fitness moments</it> for analyzing the working of Genetic Algorithms (GAs). We show that the fitness moments in any generation may be predicted from those of the initial population. Since a knowledge of the fitness moments allows us to estimate the fitness distribution of strings, this approach provides for a method of characterizing the dynamics of GAs. In particular the average fitness and fitness variance of the population in any generation may be predicted.</p><ip2>We introduce the technique of <it>fitness-based disruption</it> of solutions for improving the performance of GAs. Using fitness moments, we demonstrate the advantages of using fitness-based disruption. We also present experimental results comparing the performance of a standard GA and GAs (CDGA and AGA) that incorporate the principle of fitness-based disruption. The experimental evidence clearly demonstrates the power of fitness based disruption.</ip2>
Search methods, genetic algorithms, fitness moments, adaptive variation of parameters, controlled disruption.

L. Patnaik and M. Srinivas, "Genetic Search: Analysis Using Fitness Moments," in IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 120-133, 1996.
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